June 14th - 19th, 2020 Seattle, WA
Description of the 1st Anti-UAV Challenge
Recently, unmanned aerial vehicle (UAV) is growing rapidly in a wide range of consumer communications and networks with their autonomy, flexibility, and a broad range of application domains. UAV applications offer possible civil and public domain applications in which single or multiple UAVs may be used. At the same time, we also need to be aware of the potential threat to our lives caused by UAV intrusion. Earlier this year, multiple instances of drone sightings halted air traffic at airports, leading to significant economic losses for airlines.This workshop focuses on state-of-the-art anti-UAV systems in a bid to safeguard flights.
The current computer vision research for UAV lacks a high-quality benchmark in dynamic environments. To mitigate this gap, this workshop presents a benchmark dataset and evaluation methodology for the area of detecting and tracking UAVs.The dataset consists of 160 high quality, Full HD video sequences (100 videos are used for test-dev and the rest are used for test-final), spanning multiple occurrences of multi-scale UAVs. This workshop also encourages participants to establish approaches to fully automatic detection and tracking of UAVs in videos.
This workshop will bring together academic and industrial experts in the field of UAVs to discuss the techniques and applications of tracking UAVs. Participants are invited to submit their original contributions, surveys, and case studies that address the works of UAV’s detection and tracking issues.
Topics of interest
The submissions are expected to deal with visual perception and processing tasks which include but are not limited to:
- Applications of computer vision on UAVs
- Strategies for searching of UAVs based on NIR and/or VIS data
- Spectrum sensing techniques for UAVs detection
- Localization and open-set identification of UAVs
- Scene understanding for UAVs
- Small/tiny object detection and tracking techniques
- Fine-grained object recognition
- Real-time deep learning inference
- Infrared image and video analysis
- Multimodal fusion techniques